INTRODUCTION
Building a language model from the ground up can seem like scaling a sheer cliff without harnesses and ropes. The desire to craft a custom model sometimes arises from the need to handle a domain-specific vocabulary or to integrate particular constraints that off-the-shelf solutions cannot satisfy. At the same time, software engineers embarking on this journey must appreciate the considerable investment of time, expertise, and compute resources it demands. This article aims to guide you, step by careful step, through the essential phases of assembling your own transformer-based language model, assuming that you have a solid foundation in Python programming and a basic understanding of machine learning concepts.
First, it is important to establish what you will need before writing a single line of model code. Access to a sufficiently large and representative text corpus is paramount, along with the ability to preprocess that data into token sequences. You will need a machine or cluster equipped with one or more GPUs (graphics processing units) supported by the deep-learning framework of your choice—here we will use PyTorch, because of its flexibility and wide adoption in research and industry. You should be familiar with concepts such as gradients, optimizer algorithms, and the basic structure of neural networks.
Beyond hardware and software, you should think about version control for your data and checkpoints, reproducibility of training runs, and how you will validate your model’s behavior as it trains. These concerns might feel ancillary compared to defining self-attention mechanisms, but in practice they often determine whether your project succeeds or flounders. With that context in place, we will next move into gathering and preparing text data, explaining how to clean, split, and tokenize the corpus so that it is ready for consumption by your custom model.
In the next section, we will explore the prerequisites and environment setup in more detail, ensuring that your workstation or cluster is properly configured for the task.
PREREQUISITES AND ENVIRONMENT SETUP
Before you begin to write any model code, it is essential to ensure that your development environment meets the demands of large-scale neural network training. You will first need to install a recent version of Python, since most deep-learning frameworks and tokenization libraries depend on features introduced in the last few releases. A virtual environment or a container system such as Docker will help you isolate this project’s dependencies from other work on your machine. Inside that isolated environment, you will install a deep-learning framework; in this guide we choose PyTorch for its straightforward API and strong community support. You will also install a tokenization library, such as Hugging Face’s tokenizers package, because writing your own byte-pair-encoding implementation is possible but distracts from the architectural focus of this article.
Next, you must verify that your hardware is recognized correctly by the framework. Even if you have one or more GPUs attached, driver mismatches or library conflicts can leave them invisible to PyTorch. A brief Python script will help you confirm that your environment sees the GPUs and that CUDA is available for acceleration. The code example below shows how to perform this check. It is presented first in plain text and then explained line by line.
Code Example Introduction
This snippet connects to the deep-learning framework, queries whether CUDA (the Nvidia accelerator interface) is available, and reports the number of GPUs detected. Running this piece of code will verify that your software stack and drivers are properly configured before you proceed with heavier training workloads.
import torch
def check_cuda_availability():
if torch.cuda.is_available():
gpu_count = torch.cuda.device_count()
print(f"CUDA is available. Number of GPUs detected: {gpu_count}")
for idx in range(gpu_count):
name = torch.cuda.get_device_name(idx)
print(f" GPU {idx}: {name}")
else:
print("CUDA is not available. Training will fall back to CPU, which may be very slow.")
if __name__ == "__main__":
check_cuda_availability()
Line-by-line Explanation
The first line imports the torch library, which provides PyTorch’s core functionality. The function definition that follows encapsulates our diagnostic checks so that the logic remains clear. Inside that function, the call to torch.cuda.is_available() returns a Boolean indicating whether the CUDA runtime is reachable. If it is true, torch.cuda.device_count() returns an integer with the number of GPUs PyTorch can see. The code then prints that count and iterates over each GPU index, querying its human-readable name with torch.cuda.get_device_name(idx) to help you verify that the correct hardware is present. If CUDA is not available, the script warns you that it will default to CPU training.
Finally, the conditional at the bottom, if name == “main”, ensures that the check only runs when the script is executed directly, and not if it is imported into another module. Save this as a file named gpu_check.py, activate your virtual environment or container shell, and run python gpu_check.py. If you receive output listing each GPU, you have confidence that your training environment is configured correctly.
DATA COLLECTION AND PREPROCESSING
When building a language model, the text data that you feed into the network is the raw material from which the model distills patterns of language. You may gather text from news articles, books, forums, or any domain-specific source that matches your intended application. It is crucial to ensure that you have the rights or license to use the data and that the text format remains consistent across documents. Once you have assembled your raw text files, you must clean them. Cleaning involves removing control characters that serve no purpose in natural language, normalizing whitespace so that multiple spaces and line breaks become uniform, and optionally mapping Unicode characters to a canonical form so that similar symbols do not fragment your vocabulary. You should decide whether capitalization carries meaning for your task; if not, you may choose to lowercase all text, whereas preserving case can help the model distinguish proper nouns from common words. After cleaning, you partition your corpus into training, validation, and test subsets so that you can evaluate your model’s ability to generalize to new text.
Neural networks cannot process raw characters or words directly. Instead, you convert cleaned text into a sequence of integer tokens using a tokenizer that defines a fixed vocabulary. A popular approach for subword modeling is byte-pair encoding, or BPE. In byte-level BPE, you begin with all possible bytes and iteratively merge the most frequent adjacent pairs to form new tokens, which lets the tokenizer handle any Unicode text robustly. The following code snippet demonstrates how to train a byte-level BPE tokenizer over your text files using the Hugging Face tokenizers library. After training, that tokenizer is capable of converting any string into a sequence of integer indices and corresponding subword tokens.
Code Example Introduction
This example shows how to initialize a ByteLevelBPETokenizer, train it on one or more text files to produce a vocabulary of subword units, save the resulting tokenizer model to disk, and then encode a sample sentence into token IDs and human-readable tokens.
from tokenizers import ByteLevelBPETokenizer
# Initialize a byte-level BPE tokenizer
tokenizer = ByteLevelBPETokenizer()
# Train the tokenizer on your text files
tokenizer.train(files=["data/corpus1.txt", "data/corpus2.txt"],
vocab_size=30000,
min_frequency=2,
special_tokens=["<s>", "<pad>", "</s>", "<unk>", "<mask>"])
# Persist the tokenizer model to disk
tokenizer.save_model("models/tokenizer")
# Load the tokenizer back and encode a sample sentence
from tokenizers import Tokenizer
tokenizer = Tokenizer.from_file("models/tokenizer/tokenizer.json")
output = tokenizer.encode("Here is an example sentence to tokenize.")
print("Token IDs:", output.ids)
print("Tokens:", output.tokens)
Line-by-Line Explanation
In the first line, the code imports the ByteLevelBPETokenizer class from the tokenizers library, which provides fast training of subword vocabularies. The next statement constructs an instance of that class with default settings. The call to tokenizer.train takes a list of file paths pointing to your cleaned text files, and it produces a vocabulary whose size is set to thirty thousand unique tokens. The parameter min_frequency equals two, which means that any subword sequence must appear at least twice in the training text to be added to the vocabulary. The list of special_tokens provides strings for the start-of-sequence marker, the padding token, the end-of-sequence marker, the unknown-token placeholder, and a mask token for tasks that require it. After training, tokenizer.save_model writes JSON and vocabulary files under the specified directory so you can reload the exact same tokenizer later.
To demonstrate usage, the snippet then shows how to load the saved tokenizer from its JSON file using the more general Tokenizer.from_file interface. Invoking tokenizer.encode on an input string returns an Encoding object, whose ids attribute is a Python list of integers representing each token’s index in the vocabulary, and whose tokens attribute is the matching list of subword strings. Printing those values lets you inspect the effect of your vocabulary choices on the tokenization of arbitrary sentences.
With your text data cleaned, split, and tokenized, you have laid the foundation for model training. In the next section, we will design the transformer-based architecture itself, beginning with the self-attention mechanism that powers modern language models.
CORE TRANSFORMER BLOCK
The core of a transformer layer consists of a multi-head self-attention mechanism followed by a position-wise feed-forward network. The self-attention mechanism allows each position in an input sequence to attend to all other positions and compute a weighted sum of their representations. Each head projects the input into separate query, key, and value spaces, computes scaled dot-product attention, and then the outputs of all heads are concatenated and projected back. After self-attention, a dropout operation is applied, and a residual connection adds the original input to the attention output; that sum is then normalized by a layer normalization layer. Next, the position-wise feed-forward network transforms each position independently through a hidden layer with a non-linear activation and then projects back to the original embedding dimension. A second residual connection and layer normalization complete the layer. Because these layers are stacked deeply, a transformer model can learn intricate patterns of dependency in language sequences.
Code Example Introduction
In the following PyTorch snippet, we implement a single transformer block encapsulating the multi-head self-attention and the feed-forward network. The constructor defines the sublayers and the forward method composes them with the necessary residual connections, normalizations, and dropout operations. This minimal block can be instantiated and used within a larger model to stack several such layers in sequence.
import torch
import torch.nn as nn
class TransformerBlock(nn.Module):
def __init__(self, embed_size, num_heads, hidden_size, dropout):
super(TransformerBlock, self).__init__()
self.attention = nn.MultiheadAttention(embed_size, num_heads, dropout=dropout)
self.norm1 = nn.LayerNorm(embed_size)
self.ffn = nn.Sequential(
nn.Linear(embed_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, embed_size)
)
self.norm2 = nn.LayerNorm(embed_size)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
attn_output, _ = self.attention(x, x, x)
x = self.norm1(x + self.dropout(attn_output))
ffn_output = self.ffn(x)
x = self.norm2(x + self.dropout(ffn_output))
return x
Line-by-Line Explanation
The first two lines import torch, the primary library for tensor computation, and import the neural network module namespace as nn so that we can refer to built-in layers and functions with a concise prefix. Defining the TransformerBlock class as a subclass of nn.Module ensures that PyTorch will track its parameters and allow us to call methods such as to move the block to GPUs or export its state.
Within the constructor, the call to nn.MultiheadAttention creates a self-attention layer that projects inputs of dimension embed_size into multiple parallel attention heads, each of which computes scaled dot-product attention and returns concatenated results. The dropout parameter applies dropout to the attention weights, which helps regularize training. The subsequent nn.LayerNorm normalizes the summed input and attention output to stabilize gradients and speed convergence.
The feed-forward network is assembled with nn.Sequential, chaining first an nn.Linear that expands each embedding from embed_size to a larger hidden_size dimension, then a non-linear activation via nn.ReLU, and finally another nn.Linear that projects the hidden state back to the original embedding dimension. This position-wise transformation enriches each token representation independently. A second nn.LayerNorm and the same nn.Dropout instance complete the sublayer definitions.
In the forward method, the input tensor x, which should have shape sequence_length by batch_size by embed_size, is fed into the attention layer as queries, keys, and values. The attention call returns both the output and the attention weights, although we ignore the latter with the underscore placeholder. After applying dropout to the attention output, adding it to the original x tensor implements the residual connection, and layer normalization is applied. Next, the normalized tensor is passed through the feed-forward network to compute ffn_output. Adding dropout to ffn_output before summing it with the input of this sublayer again builds another residual connection, followed by layer normalization. The resulting tensor is returned and can be passed into the next transformer block or downstream layers.
This minimal implementation captures the essential components of a transformer layer. In the next section, we will discuss positional encodings and how to integrate multiple blocks into a full model, as well as how to handle batching and masking for training.
POSITIONAL ENCODING AND MODEL ASSEMBLY
To enable the transformer to distinguish positions in a sequence, we add a positional encoding to each token embedding. Positional encodings inject information about the absolute position of each token so that the self-attention mechanism can learn order-dependent patterns. One common approach uses sine and cosine functions at different frequencies. After computing token embeddings from an embedding matrix, we compute a fixed positional encoding matrix and add it element-wise to the token embeddings. Once token embeddings carry position information, we can pass them through a stack of transformer blocks to build a language model. We then project the final hidden states back to vocabulary logits for next-token prediction.
Code Example Introduction
The following PyTorch snippet implements positional encodings and assembles the full language model. The TransformerLanguageModel class contains an embedding layer for tokens, a learned dropout on embeddings, a fixed positional encoding buffer, a sequence of transformer blocks, and a final linear layer that maps hidden states to vocabulary logits. The forward method accepts input token IDs, adds positional encodings, applies a causal mask so that each token only attends to previous positions, and returns unnormalized logit scores for each position in the sequence.
import math
import torch
import torch.nn as nn
class TransformerLanguageModel(nn.Module):
def __init__(self, vocab_size, embed_size, num_heads, hidden_size,
num_layers, max_seq_length, dropout):
super(TransformerLanguageModel, self).__init__()
self.token_embedding = nn.Embedding(vocab_size, embed_size)
self.position_dropout = nn.Dropout(p=dropout)
# Create positional encodings once and register as buffer
pos_encoding = torch.zeros(max_seq_length, embed_size)
position = torch.arange(0, max_seq_length).unsqueeze(1).float()
div_term = torch.exp(torch.arange(0, embed_size, 2).float()
* (-math.log(10000.0) / embed_size))
pos_encoding[:, 0::2] = torch.sin(position * div_term)
pos_encoding[:, 1::2] = torch.cos(position * div_term)
pos_encoding = pos_encoding.unsqueeze(1) # shape (max_seq_length, 1, embed_size)
self.register_buffer("pos_encoding", pos_encoding)
# Stack transformer blocks
self.layers = nn.ModuleList([
TransformerBlock(embed_size, num_heads, hidden_size, dropout)
for _ in range(num_layers)
])
self.norm = nn.LayerNorm(embed_size)
self.output_linear = nn.Linear(embed_size, vocab_size)
def forward(self, input_ids):
# input_ids shape: (seq_length, batch_size)
seq_length, batch_size = input_ids.size()
embeddings = self.token_embedding(input_ids) # shape (seq_length, batch_size, embed_size)
embeddings = embeddings + self.pos_encoding[:seq_length]
embeddings = self.position_dropout(embeddings)
# Create causal mask so position i cannot attend to j > i
mask = torch.triu(torch.ones(seq_length, seq_length), diagonal=1).bool().to(embeddings.device)
x = embeddings
for layer in self.layers:
x = layer(x, attn_mask=mask)
x = self.norm(x)
logits = self.output_linear(x) # shape (seq_length, batch_size, vocab_size)
return logits
Line-by-Line Explanation
The code begins by importing the math module for logarithms and trigonometric constants, then imports torch and the neural-network module nn. We define TransformerLanguageModel as a subclass of nn.Module so PyTorch can manage its parameters.
In the constructor, we create a token_embedding layer that maps integer token IDs to dense vectors of dimension embed_size. We follow this with a Dropout layer applied to embeddings to regularize at small rates.
To build the positional encoding matrix, we allocate a tensor of zeros with shape (max_seq_length, embed_size). We generate a column vector of positions from zero up to max_seq_length minus one, convert it to float, and compute a div_term vector of dimension embed_size/2 that determines the frequency of each sine and cosine wave. We fill even embedding dimensions with sine of position times frequency and odd dimensions with cosine. By unsqueezing a singleton batch dimension, we obtain pos_encoding of shape (max_seq_length, 1, embed_size). Registering it as a buffer ensures it moves with the model to GPU but is not a trainable parameter.
Next, we build a list of TransformerBlock instances—one for each layer. We use nn.ModuleList so that PyTorch registers each block’s parameters. After stacking the blocks, we apply a final layer normalization and define output_linear, a linear projection from the embed_size to the token vocabulary size, producing logits for next-token prediction.
In the forward method, we expect input_ids shaped (seq_length, batch_size). We look up embeddings for each token, add the corresponding slice of positional encodings, and apply dropout. To ensure causal attention, we create a boolean mask of shape (seq_length, seq_length) where positions above the main diagonal are true and indicate forbidden attentions. We then iterate through each transformer layer, passing x and the attention mask. After processing through all layers, we normalize x one last time and compute logits via the output_linear layer, returning a tensor shaped (seq_length, batch_size, vocab_size).
With the model defined, you are now ready to implement the training loop. In the next section, we will cover how to compute the language modeling loss, configure optimizers and learning-rate schedules, and run efficient, mixed-precision training across multiple GPUs or nodes.
TRAINING PIPELINE
Once your model architecture is in place, you will need to teach it to predict the next token in a sequence by running a training loop that iterates over your tokenized text. The central goal is to minimize the cross-entropy loss between the model’s output logits and the true token IDs. You will create batches of input sequences and target sequences, move data and the model to GPU(s), and perform forward and backward passes. To improve training stability and speed, you may enable mixed-precision arithmetic, and if you have more than one GPU, you can wrap the model in a data-parallel wrapper. You will also configure an optimizer such as AdamW and a learning-rate schedule that warms up at the start of training and decays afterwards.
Code Example Introduction
The following example shows a simplified training loop in PyTorch. It defines a function train_one_epoch which iterates over a DataLoader of input–target pairs. Inside the loop, the code moves tensors to the device, uses an automatic mixed-precision context to compute forward passes and losses, applies gradient scaling for stability, steps the optimizer and scheduler, and resets gradients. At the end of each epoch, it prints the average loss so you can track training progress. This snippet assumes that you have already constructed your TransformerLanguageModel, optimizer, scheduler, and DataLoader.
import torch
from torch.cuda.amp import autocast, GradScaler
from torch.utils.data import DataLoader, Dataset
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
def train_one_epoch(model, dataloader, optimizer, scheduler, device):
model.train()
scaler = GradScaler()
total_loss = 0.0
token_count = 0
for batch in dataloader:
input_ids, target_ids = batch
input_ids = input_ids.to(device)
target_ids = target_ids.to(device)
optimizer.zero_grad()
with autocast():
logits = model(input_ids)
# reshape logits to (batch_size * seq_length, vocab_size)
seq_length, batch_size, vocab_size = logits.size()
loss = torch.nn.functional.cross_entropy(
logits.view(-1, vocab_size),
target_ids.view(-1),
ignore_index=0 # assuming 0 is the pad token
)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
scheduler.step()
total_loss += loss.item() * batch_size * seq_length
token_count += batch_size * seq_length
avg_loss = total_loss / token_count
print(f"Average loss: {avg_loss:.4f}")
return avg_loss
# Example setup before calling train_one_epoch
# dataset = YourCustomDataset(...)
# dataloader = DataLoader(dataset, batch_size=8, shuffle=True)
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# model = TransformerLanguageModel(...).to(device)
# model = torch.nn.DataParallel(model) # simple multi-GPU support
# optimizer = AdamW(model.parameters(), lr=5e-5, weight_decay=0.01)
# total_steps = num_epochs * len(dataloader)
# def lr_lambda(current_step):
# warmup_steps = int(0.1 * total_steps)
# if current_step < warmup_steps:
# return float(current_step) / float(max(1, warmup_steps))
# return max(
# 0.0,
# float(total_steps - current_step) / float(max(1, total_steps - warmup_steps))
# )
# scheduler = LambdaLR(optimizer, lr_lambda)
Line-by-Line Explanation
The code begins by importing core PyTorch modules for tensor operations, automatic mixed-precision tools, data loading, optimization, and learning-rate scheduling. The function train_one_epoch takes the model, a DataLoader yielding batches of input and target token sequences, an optimizer, a scheduler, and the device on which to compute. Inside that function, model.train() places the model in training mode so that dropout layers behave correctly. Instantiating GradScaler enables dynamic scaling of loss values during mixed-precision training, which helps prevent underflow.
We initialize accumulators total_loss and token_count to track the sum of losses and the number of tokens processed so we can compute an average at the end. The loop over dataloader retrieves batches, each consisting of input_ids and target_ids tensors in shape (seq_length, batch_size). Moving these tensors to the device ensures that computations occur on GPU memory if available.
Before computing the forward pass, we zero out accumulated gradients with optimizer.zero_grad(). The autocast context temporarily casts operations to lower precision where safe, improving performance and reducing memory usage. Inside that context, calling model(input_ids) computes logits of shape (seq_length, batch_size, vocab_size). Because cross-entropy in PyTorch expects a two-dimensional input of shape (N, C), we reshape the logits to merge sequence and batch dimensions and flatten target_ids similarly. The ignore_index parameter tells the loss function to skip the padding tokens in the calculation.
After computing the loss, we call scaler.scale(loss).backward() to scale gradients before backpropagation. Then scaler.step(optimizer) applies the optimizer update at the appropriately scaled gradient, and scaler.update() adjusts the scale factor for the next iteration. Calling scheduler.step() updates the learning rate according to the predefined LambdaLR schedule.
We accumulate the raw loss multiplied by the number of tokens so that we can divide by total tokens at the end and compute an average loss per token. Printing the average loss gives you immediate feedback on whether training is proceeding as expected.
In the commented “Example setup” block, you see how to construct a DataLoader from your custom dataset implementation, select the device, instantiate the model (and wrap it with DataParallel for simple multi-GPU support), configure the AdamW optimizer with weight decay for regularization, compute the total number of training steps, and define a learning-rate lambda function that implements a linear warmup phase followed by a linear decay. Finally, a LambdaLR scheduler is created to adjust the learning rate each step.
With this training pipeline now defined, you can launch full training runs. In the next section, we will describe how to monitor training with validation routines, compute perplexity, and perform inference to inspect the quality of generated samples.
EVALUATION AND INFERENCE
After you have trained your model for a number of epochs, it is important to measure how well it generalizes to unseen text. A common way to do this is to compute the average cross-entropy loss on a held-out validation set and then convert that loss into perplexity by exponentiating it. Perplexity gives an interpretable score of how many plausible tokens the model considers at each step. Lower perplexity indicates that the model assigns higher probability to the ground-truth next tokens. To inspect qualitative behavior, you will also run inference by seeding the model with a prompt and sampling tokens according to the model’s output distribution. Sampling can be as simple as picking the most likely token at each position (greedy decoding) or more expressive by applying temperature scaling or restricting to a top-k subset of probable tokens.
Code Example Introduction
The snippet below defines two functions: one to evaluate average loss and compute perplexity on a DataLoader, and another to generate text given a prompt string. The evaluation function moves the model into evaluation mode, iterates without tracking gradients, accumulates loss over the validation batches, and finally prints the resulting perplexity. The generation function uses a simple loop to feed back the model’s outputs as inputs, applying temperature and top-k filtering to sample more diverse continuations.
import torch
import torch.nn.functional as F
def evaluate(model, dataloader, device):
model.eval()
total_loss = 0.0
token_count = 0
with torch.no_grad():
for input_ids, target_ids in dataloader:
input_ids = input_ids.to(device)
target_ids = target_ids.to(device)
logits = model(input_ids)
seq_length, batch_size, vocab_size = logits.size()
loss = F.cross_entropy(
logits.view(-1, vocab_size),
target_ids.view(-1),
ignore_index=0
)
total_loss += loss.item() * batch_size * seq_length
token_count += batch_size * seq_length
avg_loss = total_loss / token_count
perplexity = torch.exp(torch.tensor(avg_loss))
print(f"Validation perplexity: {perplexity:.2f}")
return perplexity
def generate_text(model, tokenizer, prompt, max_length, device, temperature=1.0, top_k=50):
model.eval()
input_ids = torch.tensor(tokenizer.encode(prompt).ids).unsqueeze(1).to(device)
generated = input_ids
with torch.no_grad():
for _ in range(max_length):
logits = model(generated)
logits = logits[-1, 0, :] / temperature
topk_logits, topk_indices = torch.topk(logits, top_k)
probabilities = F.softmax(topk_logits, dim=-1)
next_token = topk_indices[torch.multinomial(probabilities, num_samples=1)]
generated = torch.cat([generated, next_token.unsqueeze(1)], dim=0)
generated_ids = generated.squeeze(1).tolist()
text = tokenizer.decode(generated_ids)
print(f"Generated text:\n{text}")
return text
Line-by-Line Explanation
The evaluate function sets the model to evaluation mode so that dropout and other training-only behaviors are disabled. By wrapping the loop in torch.no_grad, the code ensures that no gradients are computed, reducing memory overhead. Inside each batch, the inputs and targets move to the device. The model produces logits whose shape is sequence length by batch size by vocabulary size. The cross-entropy loss flattens these dimensions and ignores any padding tokens. Accumulating loss weighted by the number of tokens and dividing by the total token count yields the average loss per token. Taking the exponential produces the perplexity score.
The generate_text function begins by encoding the prompt string into token IDs and reshaping them for a batch size of one. In the generation loop, the model processes all tokens so far and returns logits; selecting only the last position’s logits gives the distribution for the next token. Dividing by temperature sharpens or flattens the distribution, and torch.topk selects the highest-probability candidates. Applying softmax over those top-k logits converts them into a proper probability distribution, from which torch.multinomial samples one index. That sampled token is appended to the generated sequence, and the loop continues until the desired maximum length. Finally, decoding the list of IDs back to a string produces the text output.
DEPLOYMENT AND SERVING
Once you are satisfied with your model’s performance, you will want to save it and serve it behind an API so that applications can request generated text in real time. In PyTorch, you persist the model’s learned parameters with state_dict, and you can reload them later into the same model class. For serving, a lightweight web framework such as FastAPI offers asynchronous request handling and automatic OpenAPI schema generation. You will define a single endpoint that accepts a JSON body containing the prompt text and optional sampling parameters, runs the generation routine on the server’s GPU, and returns the resulting string in a JSON response.
Code Example Introduction
This example illustrates saving the model to disk, loading it back into memory, and creating a FastAPI app with a /generate endpoint. When a POST request arrives, the handler encodes the prompt, moves tensors to the device, runs the generation loop, decodes the output, and responds with the generated text.
# Saving and loading the model
torch.save(model.state_dict(), "model_checkpoint.pt")
model = TransformerLanguageModel(...)
model.load_state_dict(torch.load("model_checkpoint.pt"))
model.to(device)
# FastAPI serving app
from fastapi import FastAPI
from pydantic import BaseModel
class GenerateRequest(BaseModel):
prompt: str
max_length: int = 50
temperature: float = 1.0
top_k: int = 50
app = FastAPI()
@app.post("/generate")
async def generate(req: GenerateRequest):
input_ids = torch.tensor(tokenizer.encode(req.prompt).ids).unsqueeze(1).to(device)
generated = input_ids
with torch.no_grad():
for _ in range(req.max_length):
logits = model(generated)
logits = logits[-1, 0, :] / req.temperature
topk_logits, topk_indices = torch.topk(logits, req.top_k)
probs = F.softmax(topk_logits, dim=-1)
next_token = topk_indices[torch.multinomial(probs, num_samples=1)]
generated = torch.cat([generated, next_token.unsqueeze(1)], dim=0)
text = tokenizer.decode(generated.squeeze(1).tolist())
return {"generated_text": text}
Line-by-Line Explanation
First, torch.save writes the model’s state_dict—a mapping from parameter names to tensors—to a file. To reload, you instantiate the model class with the same architecture, call load_state_dict on the loaded dictionary, and move the model to the appropriate device. In FastAPI, you define a Pydantic BaseModel subclass to validate incoming JSON for the prompt and sampling settings. Decorating a function with @app.post registers it to handle POST requests at the /generate path. Inside the handler, the code closely mirrors the generate_text function from before but adapts to parameters from the request object. Finally, returning a dict causes FastAPI to serialize it as JSON automatically.
EXTENSIONS AND FURTHER READING
After you have built, trained, and served a basic transformer language model, there are many avenues to explore. You may integrate a retrieval component to ground generation in external documents, you may fine-tune the model on conversational or task-specific data, or you may experiment with parameter-efficient techniques such as adapters or LoRA to reduce compute costs for continual learning. You might investigate variants of the transformer architecture, including sparse attention, memory-augmented layers, or even more recent decoder-only designs. For reliable references and deeper dives, the authoritative transformer paper provides the theoretical foundation, the Hugging Face Transformers library offers production-grade implementations, and the EleutherAI community publishes benchmarks and open weights for large models. Always pay attention to licensing terms and ethical considerations around data use and model outputs.
With these steps and examples, you now have a comprehensive roadmap for building your own transformer-based language model.
OPTIMIZING TRAINING THROUGHPUT
To reduce the time it takes to train large models, you can combine several techniques that work together to improve hardware utilization and numerical efficiency. One powerful strategy is to enable mixed-precision training, which uses half-precision floating point for compute-intensive matrix operations and full-precision for accumulation. This approach reduces memory bandwidth and increases arithmetic throughput on modern GPUs without sacrificing final model quality. Another technique is gradient accumulation, which simulates larger batch sizes by splitting each optimizer step across multiple forward-and-backward passes. This means that even if your GPU memory limits you to small micro-batches, you can still enjoy the stability benefits of a larger effective batch size. Finally, distributed data parallelism allows you to split each batch across multiple devices or nodes, synchronizing gradients after every step so that each replica contributes to a single global model update.
Code Example Introduction
The following snippet demonstrates how to wrap your model in PyTorch’s DistributedDataParallel, enable automatic mixed-precision with a GradScaler, and perform gradient accumulation to simulate an effective batch size larger than your GPU can hold at once. This code assumes that you have already initialized the process group for distributed training and that each process is bound to a single GPU.
import torch
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import GradScaler, autocast
def train_epoch_ddp(model, dataloader, optimizer, scheduler, device, accumulation_steps):
model = DDP(model, device_ids=[device])
scaler = GradScaler()
total_loss = 0.0
token_count = 0
optimizer.zero_grad()
for step, batch in enumerate(dataloader):
input_ids, target_ids = batch
input_ids = input_ids.to(device)
target_ids = target_ids.to(device)
with autocast():
logits = model(input_ids)
seq_length, batch_size, vocab_size = logits.size()
loss = torch.nn.functional.cross_entropy(
logits.view(-1, vocab_size),
target_ids.view(-1),
ignore_index=0
) / accumulation_steps
scaler.scale(loss).backward()
if (step + 1) % accumulation_steps == 0:
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
scheduler.step()
total_loss += loss.item() * batch_size * seq_length * accumulation_steps
token_count += batch_size * seq_length
avg_loss = total_loss / token_count
print(f"Average loss with DDP and accumulation: {avg_loss:.4f}")
return avg_loss
Line-by-Line Explanation
First, the code imports the necessary modules for distributed parallelism, mixed-precision scaling, and autocasting. Within the train_epoch_ddp function, we wrap the provided model in DistributedDataParallel, specifying the GPU device for this process. Creating a GradScaler enables safe use of half-precision arithmetic where appropriate. We initialize total_loss and token_count to accumulate metrics across the epoch, and then zero out optimizer gradients before entering the batch loop.
Inside the loop, each batch of input and target token IDs moves to the assigned GPU. The autocast context automatically casts eligible operations to float16, which accelerates computation on GPUs that support tensor cores. We compute the cross-entropy loss and immediately divide by accumulation_steps to distribute the gradient contribution evenly across multiple micro-batches. Calling scaler.scale(loss).backward queues a scaled backward pass, and only when we have processed accumulation_steps micro-batches do we perform the optimizer step. At that moment, we call scaler.step to apply gradients at the correct scale, update the scaler for the next iteration, zero the optimizer gradients for the next accumulation cycle, and step the learning-rate scheduler.
We accumulate the unscaled loss (multiplied by accumulation_steps to approximate the true batch loss) for reporting, and after processing all steps, we compute and print the average loss. By combining distributed data parallelism, gradient accumulation, and mixed-precision, this loop maximizes GPU throughput and allows training of larger models than single-GPU memory would otherwise permit.
ADVANCED SAMPLING STRATEGIES
When generating text from your trained model, choosing sampling parameters carefully can greatly affect the coherence and diversity of outputs. Temperature controls how “greedy” the model is: lower values concentrate probability on the highest-scoring tokens, producing more predictable but potentially repetitive text, whereas higher values flatten the distribution and introduce more randomness. Top-k sampling restricts the candidate set to the k highest-probability tokens, preventing unlikely tokens from being chosen. Top-p, or nucleus sampling, dynamically selects the smallest set of tokens whose cumulative probability exceeds a threshold p, adapting to distribution shape so that high-confidence predictions remain sharp while uncertain contexts allow more diversity.
Code Example Introduction
The snippet below shows how to implement temperature scaling, top-k sampling, and top-p nucleus sampling during generation. You will see how to apply each method independently and how to combine them so that you sample only from a filtered distribution.
import torch
import torch.nn.functional as F
def sample_next_token(logits, temperature=1.0, top_k=None, top_p=None):
scaled_logits = logits / temperature
if top_k is not None:
values, indices = torch.topk(scaled_logits, top_k)
probs = F.softmax(values, dim=-1)
next_token = indices[torch.multinomial(probs, 1)]
elif top_p is not None:
sorted_logits, sorted_indices = torch.sort(scaled_logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
cutoff = cumulative_probs > top_p
cutoff_index = torch.where(cutoff)[0][0].item() + 1
filtered_logits = sorted_logits[:cutoff_index]
filtered_indices = sorted_indices[:cutoff_index]
probs = F.softmax(filtered_logits, dim=-1)
next_token = filtered_indices[torch.multinomial(probs, 1)]
else:
probs = F.softmax(scaled_logits, dim=-1)
next_token = torch.multinomial(probs, 1)
return next_token.item()
Line-by-Line Explanation
The function sample_next_token takes the raw logits for a single position and optionally temperature, top_k, and top_p parameters. We first divide the logits by the temperature value to control randomness. If top_k is provided, we identify the k largest logits and their corresponding token indices. We apply softmax to those values to obtain a probability distribution over the k candidates, from which we sample one token via torch.multinomial.
If top_k is not used but top_p is set, we sort logits in descending order and compute the cumulative sum of their softmaxed probabilities. We then find the smallest index at which the cumulative probability exceeds top_p, which defines our nucleus set. By slicing the sorted logits and indices up to that cutoff, we restrict sampling to tokens whose combined probability mass meets the threshold. We apply softmax to the filtered logits and sample one index from that distribution.
If neither top_k nor top_p is specified, we fall back to sampling from the full softmax distribution. The function returns a Python integer for the chosen token ID so you can plug it into your generation loop. By adjusting temperature, top_k, and top_p, you can explore a continuum of generation behaviors from deterministic to highly diverse.
INTEGRATING THE MODEL INTO A LARGER APPLICATION
Embedding your language model into a production application often requires wiring it into logging, authentication, and batching layers. You will typically front your inference endpoint with a service that handles rate limiting, input validation, and asynchronous batching of multiple user requests to improve throughput. For example, you might accumulate incoming prompts for a few milliseconds, batch them into a single model call, and then split the outputs back to individual responses. Monitoring is also essential: you should log input characteristics, response latencies, and sampling parameters, and you should track user feedback or automated quality checks to detect drift or abuse over time.
Code Example Introduction
In the following example, we extend the FastAPI application to batch multiple generation requests that arrive within a short time window. A background task gathers pending prompts, executes a single model inference for the batch, and dispatches results to waiting request handlers. This pattern increases GPU utilization under bursty traffic without requiring more hardware.
from fastapi import FastAPI, BackgroundTasks
from pydantic import BaseModel
import asyncio
import torch
class PromptRequest(BaseModel):
prompt: str
max_length: int = 50
app = FastAPI()
request_queue = []
response_futures = []
@app.post("/batch_generate")
async def batch_generate(req: PromptRequest, background_tasks: BackgroundTasks):
loop = asyncio.get_event_loop()
future = loop.create_future()
request_queue.append(req)
response_futures.append(future)
if len(request_queue) == 1:
background_tasks.add_task(process_batch)
return await future
async def process_batch():
await asyncio.sleep(0.01)
batch = list(request_queue)
request_queue.clear()
futures = list(response_futures)
response_futures.clear()
input_ids = [torch.tensor(tokenizer.encode(r.prompt).ids) for r in batch]
input_tensor = torch.nn.utils.rnn.pad_sequence(input_ids, padding_value=0).unsqueeze(1).to(device)
logits = model(input_tensor)
outputs = []
for i, r in enumerate(batch):
generated = sample_sequence_from_logits(logits[:, i, :], r.max_length)
outputs.append(tokenizer.decode(generated))
for future, text in zip(futures, outputs):
future.set_result({"generated_text": text})
Line-by-Line Explanation
We import FastAPI, BackgroundTasks for scheduling, and asyncio for asynchronous primitives. The PromptRequest class defines the expected JSON schema. We maintain two global lists: one for incoming requests and another for asyncio futures that represent the eventual responses.
When the first request arrives, we append it and its future to the queues and schedule process_batch as a background task. Subsequent requests that arrive before process_batch runs will also be queued. The small asyncio.sleep delay gives a short window to batch requests.
Inside process_batch, we copy and clear the queues so that new requests can start filling the next batch. We encode each prompt, pad the resulting sequences into a single tensor, and move it to the device for inference. After obtaining logits, we loop through each batch position, generate a sequence using your chosen sampling function, and decode the token IDs back to text. Finally, we fulfill each future with the generated result, which unblocks the waiting request handlers.
By incorporating batching logic, asynchronous scheduling, and careful queue management, this integration pattern allows your application to serve many concurrent users efficiently while leveraging the model’s full GPU throughput.
With these advanced techniques in place for training, sampling, and integration, your transformer language model is ready for real-world use.
REINFORCEMENT LEARNING FROM HUMAN FEEDBACK
When a language model has learned to predict tokens from large text corpora, it may still produce outputs that are misaligned with user expectations or societal norms. Reinforcement learning from human feedback, or RLHF, augments supervised pre-training by allowing human evaluators to score model outputs and then using those scores as rewards to fine-tune the model. In this way, the model learns not just to continue text plausibly but to prefer continuations that humans judge helpful or harmless.
Code Example Introduction
The snippet below illustrates a simplified proximal policy optimization loop that uses rewards derived from human preference scores. You will see how to collect trajectories of generated text, compute advantages relative to a value baseline, and update the policy network—the language model—while constraining updates to remain close to its pretrained behavior.
import torch
from torch.optim import Adam
from torch.distributions import Categorical
def train_rlhf(policy_model, value_model, tokenizer, prompts, human_rewards,
optimizer, clip_epsilon=0.2, gamma=0.99):
policy_model.train()
value_model.eval()
all_losses = []
for prompt, reward in zip(prompts, human_rewards):
# Encode prompt and generate a sequence
input_ids = torch.tensor(tokenizer.encode(prompt).ids).unsqueeze(0)
logits = policy_model(input_ids)
probs = torch.softmax(logits[:, -1, :], dim=-1)
dist = Categorical(probs)
action = dist.sample()
log_prob = dist.log_prob(action)
# Estimate value and compute advantage
value = value_model(input_ids).squeeze()
advantage = reward + gamma * value.detach() - value
# Compute policy loss with clipping
ratio = torch.exp(log_prob - log_prob.detach())
clipped_ratio = torch.clamp(ratio, 1 - clip_epsilon, 1 + clip_epsilon)
policy_loss = -torch.min(ratio * advantage, clipped_ratio * advantage).mean()
# Backpropagate and update policy
optimizer.zero_grad()
policy_loss.backward()
optimizer.step()
all_losses.append(policy_loss.item())
average_loss = sum(all_losses) / len(all_losses)
print(f"RLHF policy update average loss: {average_loss:.4f}")
return average_loss
Line-by-Line Explanation
The code begins by importing PyTorch’s tensor library and the Adam optimizer, as well as the Categorical distribution to sample tokens. The function train_rlhf takes the policy model (our language model to fine-tune), a value model (which estimates expected reward of a state), a tokenizer, lists of prompts and corresponding human-provided reward scores, and an optimizer.
Inside the loop, each prompt string is tokenized and reshaped for a batch size of one before being passed through the policy model. From the model’s last‐token logits, we compute a probability distribution with softmax and wrap it in a Categorical object, which lets us sample an action token and compute its log probability. Next, the value model predicts a baseline value for the prompt, and we subtract it from the observed human reward (discounted by gamma) to yield an advantage estimate.
The policy gradient objective in PPO uses a probability ratio between the new policy and the old one; here we approximate the old log probability with log_prob.detach() so that gradients do not flow through it. Clipping that ratio within [1−ε, 1+ε] prevents overly large policy updates that could catastrophically alter the model’s behavior. We take the minimum of the unclipped and clipped objectives, average across the batch, and negate the result for gradient descent.
Finally, we clear previous gradients, backpropagate the policy loss, and step the optimizer. We accumulate and report the mean loss across all prompt–reward pairs as a progress indicator. By iterating this procedure with fresh human feedback, the model gradually aligns its generation with desired qualities.
MIXTURE-OF-EXPERTS ARCHITECTURES
Mixture-of-experts models divide a large network into multiple smaller “expert” sub-networks and use a learned gating mechanism to select a sparse subset of experts for each input. This approach enables the total parameter count to grow dramatically while keeping the amount of computation per token manageable. The gating network examines the token’s hidden representation and produces a probability distribution over experts, from which the top k experts are chosen, their outputs computed in parallel, and then combined.
Code Example Introduction
The following snippet implements a simple MoE layer with two experts. A linear gating network computes selection scores, and we apply a top-1 selection so that only the single most relevant expert is invoked for each token. This design illustrates the core idea of sparse expert routing.
import torch
import torch.nn as nn
import torch.nn.functional as F
class MoELayer(nn.Module):
def __init__(self, embed_dim, expert_dim, num_experts):
super(MoELayer, self).__init__()
self.num_experts = num_experts
self.experts = nn.ModuleList([
nn.Sequential(nn.Linear(embed_dim, expert_dim), nn.ReLU(), nn.Linear(expert_dim, embed_dim))
for _ in range(num_experts)
])
self.gate = nn.Linear(embed_dim, num_experts)
def forward(self, x):
# x shape: (seq_len, batch, embed_dim)
gate_logits = self.gate(x) # shape: (seq_len, batch, num_experts)
gate_probs = F.softmax(gate_logits, dim=-1) # convert to probabilities
expert_idx = torch.argmax(gate_probs, dim=-1) # select top expert per token
outputs = torch.zeros_like(x)
for i in range(self.num_experts):
mask = (expert_idx == i).unsqueeze(-1) # mask for tokens routed to expert i
if mask.any():
expert_out = self.experts[i](x[mask.expand_as(x)])
outputs[mask.expand_as(x)] = expert_out
return outputs
Line-by-Line Explanation
We import PyTorch’s tensor operations and the neural network module. The MoELayer class takes the embedding dimension, an intermediate expert dimension, and the number of experts as parameters. We create a ModuleList of expert networks, each a simple two-layer feed-forward subnetwork with a ReLU activation. The gating network is a linear projection from the same embedding dimension to a logit for each expert.
In forward, we receive x with shape sequence length by batch by embedding size. Passing x through the gate yields a logits tensor; softmax converts these to expert-selection probabilities. We then choose the single expert with the highest probability for each token by argmax. To compute the output, we allocate a zero tensor of the same shape and loop over each expert index. We build a boolean mask for tokens assigned to that expert, select the corresponding slices of x, pass them through the expert’s feed-forward network, and write the results back into the output tensor at the masked positions. The result is that each token’s representation is transformed by one expert, and the full sequence of outputs is returned.
QUANTIZATION AND MODEL COMPRESSION
Once your model is trained, you may wish to reduce its memory footprint and improve inference latency by quantizing weights from 32-bit floating point to lower-precision formats. Dynamic quantization converts weights of linear layers to 8-bit integers at runtime, leaving activations in floating point. In PyTorch, this process requires minimal code changes and can yield substantial speedups on CPUs with little to no loss in quality.
Code Example Introduction
This example shows how to apply dynamic quantization to all linear layers in your transformer language model. After quantization, the model can be saved and loaded normally, and inference calls will use optimized integer kernels.
import torch
import torch.nn as nn
import torch.quantization as quant
# Assume `model` is your trained TransformerLanguageModel
quantized_model = quant.quantize_dynamic(
model,
{nn.Linear},
dtype=torch.qint8
)
torch.save(quantized_model.state_dict(), "quantized_model.pt")
PARAMETER-EFFICIENT FINE-TUNING
Rather than updating all parameters of a large pretrained model, you can achieve task adaptation by inserting and training a small number of additional parameters. Techniques such as adapter modules or low-rank adaptation (LoRA) add lightweight layers that capture task-specific adjustments, leaving the majority of original weights frozen. This approach reduces memory and compute requirements during fine-tuning while preserving the benefits of scale.
Code Example Introduction
Below we define a simple adapter module and show how to integrate it into each transformer block by adding a residual adapter after the feed-forward network. During task fine-tuning, only the adapter’s parameters are updated.
import torch.nn as nn
class Adapter(nn.Module):
def __init__(self, embed_dim, adapter_dim):
super(Adapter, self).__init__()
self.down_proj = nn.Linear(embed_dim, adapter_dim)
self.up_proj = nn.Linear(adapter_dim, embed_dim)
def forward(self, x):
hidden = torch.relu(self.down_proj(x))
return x + self.up_proj(hidden)
# To integrate into TransformerBlock:
# after the feed-forward and second normalization, call adapter_layer(x)
Line-by-Line Explanation
The Adapter class inherits from nn.Module. In the constructor, we define a down-projection linear layer that reduces the embedding dimension to a smaller adapter dimension, and an up-projection that restores it. In forward, we apply a ReLU activation to the down-projected tensor, then up-project it and add it back to the original input x to form a residual connection. To use this in your model, instantiate an Adapter for each transformer block and invoke it immediately after the block’s feed-forward sublayer. Because only the adapter layers’ parameters require gradients, fine-tuning on a new task is both memory-efficient and fast.
ETHICAL AND SAFETY CONSIDERATIONS
Building and deploying large language models entails responsibilities around bias, misinformation, and harmful content. You should curate your training data to minimize toxic or unrepresentative text, implement filters or classifiers to catch unsafe outputs, and monitor real-world usage to detect failure modes. Open discussions with stakeholders and transparency about model limitations can help ensure that your system is used responsibly.
CONCLUSIONS
Designing and training your own transformer-based language model is a multifaceted endeavor that spans data collection, architectural design, optimization, and deployment. Beginning with careful data preprocessing and a clear definition of your model’s dimensions, you assemble transformer blocks with self-attention and positional encodings. Through thoughtful training loops that leverage mixed-precision, gradient accumulation, and distributed parallelism, you teach the model to predict tokens efficiently at scale. Advanced techniques such as reinforcement learning from human feedback refine the model’s behavior to align with human preferences, while sparse mixture-of-experts architectures and parameter-efficient adapters let you explore models of immense capacity without prohibitive compute. Quantization and model compression make inference feasible even on constrained hardware, and careful serving patterns ensure that your model can deliver responses reliably in production. By following the roadmap laid out in this article, you now possess the foundational knowledge and practical examples to craft, train, and serve a state-of-the-art language model.
LITERATURE REFERENCES
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention Is All You Need. In: Advances in Neural Information Processing Systems. 2017.
Stiennon N, Ouyang L, Wu J, Ziegler D, Lowe R, Voss C, Radford A, Amodei D, Christiano P. Learning to Summarize with Human Feedback. In: Advances in Neural Information Processing Systems. 2020.
Fedus W, Zoph B, Shazeer N. Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity. arXiv preprint arXiv:2101.03961. 2021.
Houlsby N, Giurgiu A, Jastrzebski S, Morrone B, De Laroussilhe Q, Gesmundo A, Attariyan M, Gelly S. Parameter-Efficient Transfer Learning for NLP. In: International Conference on Machine Learning. 2019.
Shazeer N, Mirhoseini A, Maziarz K, Davis A, Le Q, Hinton G, Dean J. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. arXiv preprint arXiv:1701.06538. 2017.
Zafrir O, Boudoukh G, Izsak P, Wasserblat M. Q8BERT: Quantized BERT for Efficient Inference. In: Empirical Methods in Natural Language Processing. 2019.
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